fitVsDatCorrelation=0.853930857238086
cont.fitVsDatCorrelation=0.249817805799663

fstatistic=13125.8005307471,52,692
cont.fstatistic=3781.62820074750,52,692

residuals=-0.384794664243918,-0.0758935875789567,-0.00352479106083602,0.0745262744733071,0.948611789250477
cont.residuals=-0.458291622360122,-0.180535381648102,-0.0521143248134148,0.145642021533495,1.23537974633438

predictedValues:
Include	Exclude	Both
Lung	61.2136585747433	64.8141447452473	61.0874935531855
cerebhem	63.8096838917172	69.3272916997365	49.2228614361981
cortex	57.5122623947294	61.0104305909895	49.2155996397144
heart	61.2936350489418	69.9687135497116	53.1668478733079
kidney	62.544464528934	69.5783224985969	51.717918779388
liver	67.5794162849387	78.2118641856201	57.692007710736
stomach	64.2100181788601	70.4805780899788	56.9491767568102
testicle	62.3402928291905	70.6505561298465	51.6094734152042


diffExp=-3.60048617050403,-5.5176078080193,-3.49816819626012,-8.67507850076983,-7.03385796966285,-10.6324479006814,-6.27055991111872,-8.31026330065598
diffExpScore=0.981664318701217
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,0,0
diffExp1.4Score=0
diffExp1.3=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	62.9938034868651	60.1862044464862	58.5004645191937
cerebhem	63.2334331812708	60.8379437611029	54.9485866792163
cortex	62.8086627077213	56.8652930205731	77.7671921443785
heart	63.0428582608254	56.443362319467	72.8720023155548
kidney	63.3758467058236	62.8466825496215	60.6860885659592
liver	65.9348926960918	64.3518017307807	60.4276054698224
stomach	61.9004299984421	62.5868318672629	62.4168008578282
testicle	60.1269099520754	60.6232035318886	56.9180434862214
cont.diffExp=2.80759904037890,2.39548942016788,5.9433696871482,6.59949594135836,0.5291641562021,1.58309096531116,-0.686401868820788,-0.496293579813191
cont.diffExpScore=1.06939543809573

cont.diffExp1.5=0,0,0,0,0,0,0,0
cont.diffExp1.5Score=0
cont.diffExp1.4=0,0,0,0,0,0,0,0
cont.diffExp1.4Score=0
cont.diffExp1.3=0,0,0,0,0,0,0,0
cont.diffExp1.3Score=0
cont.diffExp1.2=0,0,0,0,0,0,0,0
cont.diffExp1.2Score=0

tran.correlation=0.93226670727703
cont.tran.correlation=0.323006396350672

tran.covariance=0.00309602090584360
cont.tran.covariance=0.000359124948908496

tran.mean=65.9090833263614
cont.tran.mean=61.7598850135186

weightedLogRatios:
wLogRatio
Lung	-0.236784004742258
cerebhem	-0.348103508347859
cortex	-0.241000207966445
heart	-0.553562236365055
kidney	-0.446461660527194
liver	-0.626314307014515
stomach	-0.392162483782801
testicle	-0.524976094610625

cont.weightedLogRatios:
wLogRatio
Lung	0.187854916306184
cerebhem	0.159402882294834
cortex	0.406616641194449
heart	0.452097319485766
kidney	0.0347535473938687
liver	0.101501243290335
stomach	-0.0455561953295869
testicle	-0.0337075763649661

varWeightedLogRatios=0.0205238231037500
cont.varWeightedLogRatios=0.0351093334739729

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.1567245192786	0.0728501047309231	57.0585935961486	7.34050811443386e-264	***
df.mm.trans1	0.213483209839600	0.065430047576328	3.26277020646453	0.00115749359845585	** 
df.mm.trans2	-0.181250084996027	0.0601718149617703	-3.01220904024888	0.00268810735240099	** 
df.mm.exp2	0.324798383821624	0.0824204849046174	3.94074827632357	8.94753564958822e-05	***
df.mm.exp3	0.093245338339981	0.0824204849046174	1.13133692974375	0.258305217101615	   
df.mm.exp4	0.216702106241106	0.0824204849046174	2.62922629600989	0.00874798876730628	** 
df.mm.exp5	0.258939434263095	0.0824204849046174	3.14168782873284	0.00175138999352742	** 
df.mm.exp6	0.344019096927911	0.0824204849046174	4.17395138266941	3.37540880856577e-05	***
df.mm.exp7	0.201750144930415	0.0824204849046174	2.44781555415373	0.0146199112680317	*  
df.mm.exp8	0.273061653859295	0.0824204849046174	3.31303139232073	0.000970938002738134	***
df.mm.trans1:exp2	-0.283263763795795	0.0789116064821028	-3.58963372339961	0.000354534438411559	***
df.mm.trans2:exp2	-0.257483598174949	0.0686837374205145	-3.74882916750019	0.000192502891118821	***
df.mm.trans1:exp3	-0.155617498177731	0.0789116064821028	-1.97204828434237	0.0490019135019052	*  
df.mm.trans2:exp3	-0.153724358133054	0.0686837374205145	-2.23814783391707	0.0255287242214276	*  
df.mm.trans1:exp4	-0.215396445440113	0.0789116064821028	-2.72959143835153	0.00650248802946425	** 
df.mm.trans2:exp4	-0.140177776036793	0.0686837374205145	-2.04091654445882	0.0416380593416668	*  
df.mm.trans1:exp5	-0.237432041949256	0.0789116064821028	-3.0088354873767	0.00271773961604176	** 
df.mm.trans2:exp5	-0.188010236433047	0.0686837374205145	-2.73733264225211	0.00635304373763503	** 
df.mm.trans1:exp6	-0.245085997016047	0.0789116064821028	-3.10582952168934	0.00197497232220355	** 
df.mm.trans2:exp6	-0.156121607599304	0.0686837374205145	-2.27305055698198	0.0233288567754168	*  
df.mm.trans1:exp7	-0.153961243952133	0.0789116064821028	-1.95105955658185	0.0514531542900845	.  
df.mm.trans2:exp7	-0.117936823795019	0.0686837374205145	-1.71709968362603	0.086408403863912	.  
df.mm.trans1:exp8	-0.25482402618237	0.0789116064821028	-3.22923379135824	0.00129988151578884	** 
df.mm.trans2:exp8	-0.186839535784854	0.0686837374205145	-2.7202878410785	0.00668628273577045	** 
df.mm.trans1:probe2	0.186517046138589	0.0394558032410514	4.72723986884975	2.75834729304796e-06	***
df.mm.trans1:probe3	-0.501190906139852	0.0394558032410514	-12.7025903661845	2.22043228938553e-33	***
df.mm.trans1:probe4	-0.353285739335717	0.0394558032410514	-8.95396140277141	3.13111674861016e-18	***
df.mm.trans1:probe5	-0.402799559599678	0.0394558032410514	-10.2088799748623	6.89030676263766e-23	***
df.mm.trans1:probe6	0.195237638183887	0.0394558032410514	4.94826165345316	9.41233866625921e-07	***
df.mm.trans1:probe7	-0.313520740954642	0.0394558032410514	-7.94612490941364	7.81635335362147e-15	***
df.mm.trans1:probe8	-0.0933976109468242	0.0394558032410514	-2.36714509083038	0.0182001236547259	*  
df.mm.trans1:probe9	-0.412740425504408	0.0394558032410514	-10.4608293736364	7.07350404222938e-24	***
df.mm.trans1:probe10	-0.498522123743397	0.0394558032410514	-12.6349505723588	4.47074375030562e-33	***
df.mm.trans1:probe11	-0.254440183497780	0.0394558032410514	-6.44873916121547	2.11704061461146e-10	***
df.mm.trans1:probe12	-0.199600423459732	0.0394558032410514	-5.05883563541446	5.40993414425549e-07	***
df.mm.trans1:probe13	-0.375894107182362	0.0394558032410514	-9.52696628391707	2.66413132238849e-20	***
df.mm.trans1:probe14	-0.226257003617707	0.0394558032410514	-5.73444170520144	1.46189520070165e-08	***
df.mm.trans1:probe15	-0.496510772696816	0.0394558032410514	-12.5839732539072	7.56468509955715e-33	***
df.mm.trans1:probe16	-0.409165858777898	0.0394558032410514	-10.3702326443119	1.61102096073640e-23	***
df.mm.trans1:probe17	-0.431283364169142	0.0394558032410514	-10.9307967077557	9.12682522515006e-26	***
df.mm.trans1:probe18	-0.382318409975835	0.0394558032410514	-9.6897890442148	6.60321376272615e-21	***
df.mm.trans1:probe19	-0.229238286458525	0.0394558032410514	-5.81000176470914	9.52960300904049e-09	***
df.mm.trans1:probe20	-0.315605313153091	0.0394558032410514	-7.99895800435061	5.28336292016262e-15	***
df.mm.trans1:probe21	-0.557882784035864	0.0394558032410514	-14.1394354748662	4.65217514557681e-40	***
df.mm.trans1:probe22	-0.324035700444505	0.0394558032410514	-8.21262460340347	1.06130695127382e-15	***
df.mm.trans2:probe2	0.239291655898967	0.0394558032410514	6.06480254468621	2.17323145518091e-09	***
df.mm.trans2:probe3	0.283781407892164	0.0394558032410514	7.19238704021381	1.65763861282794e-12	***
df.mm.trans2:probe4	0.454237782667932	0.0394558032410514	11.5125721783640	3.49164415918431e-28	***
df.mm.trans2:probe5	0.389868411625242	0.0394558032410514	9.88114243279698	1.25346101389981e-21	***
df.mm.trans2:probe6	0.397265597164995	0.0394558032410514	10.0686227254820	2.40470730946734e-22	***
df.mm.trans3:probe2	-0.09762816426544	0.0394558032410514	-2.47436767841198	0.0135859083759432	*  
df.mm.trans3:probe3	-0.132323296914585	0.0394558032410514	-3.35370936706492	0.00084081819524169	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.20184013407892	0.135553552392929	30.9976393823974	5.84353017625357e-133	***
df.mm.trans1	-0.00841824962271935	0.121746913267577	-0.0691454871156986	0.94489378941354	   
df.mm.trans2	-0.157894806950622	0.111962821496614	-1.41024319358902	0.158917204232900	   
df.mm.exp2	0.0772040427201222	0.153361337777546	0.503412684311013	0.614834357612401	   
df.mm.exp3	-0.344386285269755	0.153361337777546	-2.24558738375962	0.0250452971058441	*  
df.mm.exp4	-0.283096859243626	0.153361337777546	-1.8459467252057	0.0653268707403744	.  
df.mm.exp5	0.0126216501934718	0.153361337777546	0.0823000788619869	0.934431890571917	   
df.mm.exp6	0.0801418351509445	0.153361337777546	0.522568701553661	0.6014416791319	   
df.mm.exp7	-0.0431972884341643	0.153361337777546	-0.281670002754038	0.778280767259117	   
df.mm.exp8	-0.0119220224860135	0.153361337777546	-0.0777381226506168	0.938058839932569	   
df.mm.trans1:exp2	-0.0734072396116937	0.146832302069998	-0.499939308836136	0.617276743879802	   
df.mm.trans2:exp2	-0.0664335374183406	0.127801114814621	-0.519819702001067	0.603355483182128	   
df.mm.trans1:exp3	0.341442926233864	0.146832302069998	2.3253938092661	0.0203391263096178	*  
df.mm.trans2:exp3	0.287628311774324	0.127801114814621	2.25059313599522	0.0247245079225017	*  
df.mm.trans1:exp4	0.283875279994494	0.146832302069998	1.93332990079502	0.0536031498549775	.  
df.mm.trans2:exp4	0.218891393761962	0.127801114814621	1.71275026888043	0.0872064425563688	.  
df.mm.trans1:exp5	-0.00657519232447461	0.146832302069998	-0.0447802849358043	0.964295358305588	   
df.mm.trans2:exp5	0.0306333357972375	0.127801114814621	0.23969537231089	0.81063740292438	   
df.mm.trans1:exp6	-0.0345104185149421	0.146832302069998	-0.235032877836991	0.81425274578501	   
df.mm.trans2:exp6	-0.0132200666110867	0.127801114814621	-0.103442498371495	0.917641754621368	   
df.mm.trans1:exp7	0.0256880505608426	0.146832302069998	0.174948224598403	0.861171490889102	   
df.mm.trans2:exp7	0.0823090268200574	0.127801114814621	0.644039975233773	0.519762912808671	   
df.mm.trans1:exp8	-0.0346568474407004	0.146832302069998	-0.236030130646447	0.813479130169082	   
df.mm.trans2:exp8	0.019156574806271	0.127801114814621	0.149893643995657	0.8808922029611	   
df.mm.trans1:probe2	-0.0618863933705382	0.0734161510349992	-0.842953389657209	0.399545773199871	   
df.mm.trans1:probe3	-0.0475023035464185	0.0734161510349992	-0.647027975135513	0.517828398980096	   
df.mm.trans1:probe4	-0.137172460303124	0.0734161510349992	-1.86842347861210	0.0621254018350872	.  
df.mm.trans1:probe5	-0.0250797868584234	0.0734161510349992	-0.341611300849417	0.73274714017381	   
df.mm.trans1:probe6	-0.112509955169035	0.0734161510349992	-1.53249596421091	0.125857113692880	   
df.mm.trans1:probe7	-0.0747059002210635	0.0734161510349992	-1.01756764918729	0.309239094289764	   
df.mm.trans1:probe8	-0.0699113644812171	0.0734161510349992	-0.952261368862673	0.341296931371671	   
df.mm.trans1:probe9	-0.116367483498991	0.0734161510349992	-1.58503928438738	0.113414256249938	   
df.mm.trans1:probe10	-0.0434649382689756	0.0734161510349992	-0.592035099310163	0.554020459876406	   
df.mm.trans1:probe11	-0.109209326316853	0.0734161510349992	-1.4875381612527	0.137328239860182	   
df.mm.trans1:probe12	-0.0351430136002827	0.0734161510349992	-0.478682321326396	0.632315850653003	   
df.mm.trans1:probe13	0.047484129227974	0.0734161510349992	0.646780423088881	0.517988529088777	   
df.mm.trans1:probe14	0.0087905187795891	0.0734161510349992	0.119735489475585	0.904727438581762	   
df.mm.trans1:probe15	-0.0923162941198745	0.0734161510349992	-1.25743849028350	0.209019092313299	   
df.mm.trans1:probe16	-0.0459121792907317	0.0734161510349992	-0.625368922825228	0.531935126503988	   
df.mm.trans1:probe17	-0.0756538626901704	0.0734161510349992	-1.03047982798914	0.30314484163235	   
df.mm.trans1:probe18	-0.0403082064802848	0.0734161510349992	-0.549037315522969	0.583156951133074	   
df.mm.trans1:probe19	-0.0985847880370688	0.0734161510349992	-1.34282152696988	0.179770050062201	   
df.mm.trans1:probe20	-0.0818137430397895	0.0734161510349992	-1.11438344133278	0.265501611281577	   
df.mm.trans1:probe21	-0.0532303328056942	0.0734161510349992	-0.725049352973	0.468666848478271	   
df.mm.trans1:probe22	0.00485967775554543	0.0734161510349992	0.0661935784842316	0.94724282788843	   
df.mm.trans2:probe2	0.0467306848916435	0.0734161510349992	0.636517772082139	0.524649485152348	   
df.mm.trans2:probe3	0.135173629236055	0.0734161510349992	1.84119743858017	0.0660204948661451	.  
df.mm.trans2:probe4	0.125470101608796	0.0734161510349992	1.70902587291700	0.08789452980966	.  
df.mm.trans2:probe5	0.0811529760843855	0.0734161510349992	1.10538314717286	0.269377764622001	   
df.mm.trans2:probe6	0.0929531403362483	0.0734161510349992	1.26611296051104	0.205898696681921	   
df.mm.trans3:probe2	0.0832322748573506	0.0734161510349992	1.13370523629973	0.257310813072561	   
df.mm.trans3:probe3	0.0172905767418806	0.0734161510349992	0.235514617670951	0.813879014961018